Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As a GQ (Global Quality) Package Reliability Engineer at Micron Technology Inc., you will contribute significantly to assuring the outstanding quality and durability of Micron's products. This distinctive position allows you to coordinate package qualification for new products and innovative builds, materials, and processes. Your insights will influence product release recommendations derived from package reliability test data, enabling Micron to compete worldwide. You will develop reliability test plans to evaluate risks related to excursions, conversions, and qualification concerns. Additionally, you will manage root cause analysis and corrective measures within the GQ department for package-related qualification failures and RMAs, preparing detailed reports for review. Cooperation is essential as you offer assistance to internal and external customers regarding package-related quality and reliability matters, addressing customer inquiries promptly. Job Responsibilities - Coordinate package qualification efforts for new products and innovative builds - Offer suggestions regarding product launch grounded in reliability test outcomes - Develop reliability test plans to evaluate risk related to excursions, conversions, and qualification issues - Coordinate root cause analysis and corrective action efforts within the GQ department for qualification failures and RMAs - Prepare detailed reports for discussion - Offer assistance to internal and external customers regarding package-related quality and reliability concerns Successful Candidates Will Have - Detailed understanding of semiconductor component package assembly processes and challenges - Understanding of surface mount methods and associated challenges - Understanding of mechanical and material interaction effects on IC components and/or PCB assemblies - Good understanding of the effects of thermomechanical, thermal, mechanical, and hygroscopic swelling stresses on package reliability - Practical understanding of industry-recognized reliability test techniques, acceleration models, and sampling statistics - Self-motivation, independence, and attention to detail - Strong analytical problem-solving skills, excellent multi-tasking abilities, and the ability to interact easily with other groups - Good written and verbal communication skills - Practical understanding of software systems used in backend manufacturing Qualification Requirements - Minimum of a BS degree, M.Sc, or comparable experience in Engineering or a related field - Experience in Micro Electronic Packaging, Quality/Reliability Engineering, Failure Analysis, or a related field is preferred - Fresh graduates or candidates with less than 2 years of working experience are welcome to apply About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities - from the data center to the intelligent edge and across the client and mobile user experience. To learn more, please visit micron.com/careers All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. To request assistance with the application process and/or for reasonable accommodations, please contact hrsupport_my@micron.com Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards. Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron. AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification. Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.

Role Description The Head of Compute Engineering will lead the development and operation of high-performance GPU compute infrastructure, serving AI researchers, ML teams, and fast-growing startups. This role combines deep technical expertise with engineering leadership, driving architecture decisions, infrastructure reliability, and product innovation. You will manage a team of engineers, shape the roadmap for GPU provisioning and orchestration, and work closely with customers to optimize workload performance. This hands-on leadership role offers a rare opportunity to influence both technical strategy and day-to-day operations in a cutting-edge AI infrastructure environment. You will implement scalable systems, observability frameworks, and operational processes while fostering a culture of excellence and ownership. - Lead a team of engineers across GPU compute, platform infrastructure, backend services, and internal tooling, ensuring high standards of quality and reliability. - Contribute directly to architecture and code, particularly on GPU provisioning, orchestration, and customer-facing control planes. - Own engineering delivery, including planning, prioritization, execution, and alignment with product and company goals. - Implement observability, alerting, and on-call processes to maintain SLAs and operational health. - Collaborate with early customers to gather requirements and translate them into scalable product solutions. - Hire, onboard, and mentor engineers, fostering a culture of technical excellence and ownership. - Continuously improve compute infrastructure and operational workflows to support high-performance AI workloads. Qualifications - 7+ years of engineering experience, including 1–2 years in a tech lead or engineering management role within GPU compute environments. - Practical experience with CUDA, NVIDIA drivers, MIG/vGPU, or similar GPU compute technologies. - Strong knowledge of container orchestration (Kubernetes, Docker) and infrastructure-as-code tools (Terraform, Ansible). - Expertise in GPU cluster management, bare-metal provisioning, and inference software. - Startup mindset: thrives in fast-moving, ambiguous environments and takes full ownership of outcomes. - Excellent leadership, communication, and mentoring skills to guide engineering teams. - Bonus: experience with hyperscalers, Neocloud infrastructure, job schedulers (Slurm, LSF, Ray), large-scale storage (VAST, Weka, DAOS, Ceph, AWS S3, Lustre, GPFS), networking (InfiniBand/RoCE), and GPU inference/fine-tuning workloads (vLLM, LanceDB). Benefits - Remote-first work environment within CET-aligned time zones. - Opportunity to lead GPU compute engineering in a high-growth AI infrastructure space. - Hands-on role with deep technical involvement, not just managerial responsibilities. - Attractive equity for an early-stage, high-growth company. - Direct collaboration with founders, influencing product and technical strategy. - Flexible work arrangements promoting work-life balance. Company Description

• Design, implement, and evaluate RL algorithms suited to long-horizon, sparse-reward healthcare decisioning, including policy gradient methods (PPO, A3C), value-based approaches (DQN, Q-learning), and offline RL methods (CQL, Decision Transformer). • Define and maintain the member state representation and action space, evolving both as new programs and data sources are onboarded. • Apply the Bellman equation, reward shaping, and constraint mapping to encode clinical eligibility, suppression rules, and program-specific objectives directly into the learning objective. • Manage exploration-exploitation tradeoffs appropriate for a production healthcare environment where poorly explored actions have real member impact. • Build simulation and backtesting environments to evaluate policy quality before production promotion, using historical member journey data. • Diagnose and remediate common RL failure modes: policy collapse, credit assignment errors across long member journeys, and distributional shift between training and serving populations. • Own the nightly Databricks training workflow: feature engineering from Gold Activity History and Gold Patient Profile, state vector normalization, distributed RL training via Ray RLlib, and batch scoring of all 8M eligible members. • Collaborate with the Data Engineering team (Decisioning Team 2) to ensure training inputs are correctly joined, reward signals are accurately computed from disposition outcomes, and the feature pipeline is reproducible and auditable. • Write production-quality PySpark feature engineering jobs; maintain data lineage through Databricks Unity Catalog. • Manage model artifacts, versioning, and lifecycle in the MLflow Model Registry; ensure rollback capability is maintained at all times. • Apply multi-agent RL concepts (MARL via PettingZoo) where member household or population-level coordination is required. • Implement constraint mapping to enforce hard business rules — member caps, cooldown periods, clinical eligibility — as constraints within the RL objective rather than downstream filters. • Collaborate with the Rules Engine team to ensure Drools eligibility guards and RL policy priorities are correctly aligned and do not conflict. • Partner with Decisioning Team 1 (Decision Engine, Rules Engine) to ensure model outputs integrate cleanly with the real-time decisioning hot path and that scored recommendations cached in Redis are correctly structured and interpreted. • Document model behavior, known limitations, and failure modes for clinical and compliance stakeholders; support explainability requirements for member-facing decisions.

• Lead and oversee complex transmission line designs from conceptual engineering through final design, including EFM, conductor sizing, induction, impedance, losses, and voltage drop analysis. • Provide senior-level technical expertise and direction to design staff, ensuring technical accuracy, best practices, and industry-leading design solutions. • Perform key engineering studies such as conductor economic evaluations, lightning performance, grounding calculations, and electrical clearance determinations per NESC and applicable standards. • Interpret and apply regulatory requirements including NERC, FERC, and regional standards, and ensure compliance across project deliverables. • Prepare and review technical documentation such as specifications, data sheets, RFQs, bid evaluations, vendor drawings, and purchase recommendations. • Collaborate with project managers on scopes, schedules, budgets, staffing plans, construction support, and change order management. • Perform and review QA/QC for engineering deliverables, production drawings, CAD outputs, and construction administration activities, resolving design-related field issues. • Conduct and lead field inspections, measurements, calculations, and installation reviews for both public and private clients. • Develop and present technical reports, support marketing and business development efforts, and build strong relationships with clients and contractors. • Provide mentorship, leadership, and guidance to less experienced engineers, ensuring effective cross-discipline communication and adherence to company safety, quality, and performance standards.